Estimating spreading waveform of long-code direct sequence spread spectrum signals at a low signal-to-noise ratio
In this study, the problem of estimating the spreading waveform of long-code direct sequence spread spectrum (DSSS) signals is considered. A novel spreading waveform estimation method based on a missing data model is proposed. By showing that the long-code DSSS signal can be equivalently represented as a short-code DSSS signal with missing data, the spreading waveform estimation problem can be viewed as a low-rank matrix approximation problem with missing data that can be approximately solved by the existing optimisation methods. To evaluate the performance of the author's proposed estimator, the authors also derive the Cramer–Rao lower bound (CRB) on the mean square error of spreading waveform estimators. The simulation results demonstrate that the proposed estimator approaches the CRB and provides significant performance improvement compared with the existing estimators in the case of low signal-to-noise ratio situations.
